Implementation of price-based demand response programs through a load pattern clustering process

Panapakidis, Ioannis P.; Frantza, S.I.; Papagiannis, Grigoris K.

doi:10.1049/cp.2014.1686

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…IBP assume that users are separately or conjointly committed to lower their consumption during a certain peak time period. Price based and incentive based methods are collectively called hybrid DR program [19][20][21][22][23].…”

Section: Market and Remunerationmentioning

confidence: 99%

A demand side management control strategy using Whale optimization algorithm

Sharma

2019

SN Appl. Sci.

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In recent years, demand side management programs are in the spotlight due to the evolution of the smart grid and consumer-centric policies. Demand side management program contains many objectives one of the prime objective is to manage energy demand by certain change in consumer demand. This can be achieved by various methods such as financial discount and change in behavior through imparting education to support the stressed conditions of the grid. This paper demonstrates demand side management strategies based upon strategic conservation, peak clipping and load shifting techniques for future smart grids. The grid contains large number of controllable devices. The day before strategic conservation, peak clipping and load shifting techniques discussed in this paper are mathematically derived for minimization problem. A heuristic-based Whale optimization algorithm (WOA) was developed for solving this problem of minimization. Simulations are conducted on a test smart grid that contains a variation in loads in two service areas, one with residential consumers, and another with commercial consumers. WOA proves its efficacy by comparing the results with spider monkey optimization and biogeography based optimization. The simulation results show that proposed demand side management strategies achieve substantial savings, while reducing the peak load demand of the smart grid.

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Section: Market and Remunerationmentioning

confidence: 99%

A demand side management control strategy using Whale optimization algorithm

Sharma

2019

SN Appl. Sci.

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“…RCs can benefit from an electricity bill reduction if the RCs adjust their demand according to the prices. RCs are informed about the tariffs an hour ahead of time, in order to adjust their loads [25]. Another price-based DR, the time-of-use (TOU) pricing, is an electricity model that changes with the time of day.…”

Section: Benefits and Risks Of A Smart House And Demand Response Conceptmentioning

confidence: 99%

Analysis Application of Controllable Load Appliances Management in a Smart Home

et al. 2019

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The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

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“…In a price-based DR program, users reduce their consumption as a response to the dynamics of prices imposed by the network operator or the energy market. In these types of pricing DR programs, two main variations are defined: One based on Time of Use (ToU) [31,32], and another one called real time price dynamics [33][34][35]. Comparative studies can be found on the Time of Use and real time pricing plans using a testing system that relates the generation cost of electricity with the fixation of fixed fees [36].…”

Section: Incentive-based [23]mentioning

confidence: 99%

A Review and Analysis of Trends Related to Demand Response

et al. 2018

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This paper provides a review and analysis of trends related to demand response (DR). The authors have considered six different topics for the analysis of DR trends: Users, Network Services, Markets, Complementary Programs and Distributed Energy Resources (DER). A brief summary of the consulted articles is included and the behavior of the different DR trend-related topics is shown up to the year 2017 and their projections for 2020. As a result, the characterization of the main DR topics is obtained as well as its current and future trends. Based on the results of the study, it is concluded that the topic of complementary programs is a trendsetter for current trends and it is expected that there is a future change of focus towards the users and new services.

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Implementation of price-based demand response programs through a load pattern clustering process

Cited by 6 publications

References 12 publications

A demand side management control strategy using Whale optimization algorithm

A demand side management control strategy using Whale optimization algorithm

Analysis Application of Controllable Load Appliances Management in a Smart Home

A Review and Analysis of Trends Related to Demand Response

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